| Overall Statistics |
|
Total Orders 102 Average Win 3.32% Average Loss -1.36% Compounding Annual Return 174.508% Drawdown 14.400% Expectancy 0.540 Start Equity 1000000 End Equity 1288514.89 Net Profit 28.851% Sharpe Ratio 3.092 Sortino Ratio 4.817 Probabilistic Sharpe Ratio 79.604% Loss Rate 55% Win Rate 45% Profit-Loss Ratio 2.44 Alpha 0.745 Beta 1.31 Annual Standard Deviation 0.351 Annual Variance 0.123 Information Ratio 2.491 Tracking Error 0.331 Treynor Ratio 0.828 Total Fees $1895.04 Estimated Strategy Capacity $11000000.00 Lowest Capacity Asset NFLX SEWJWLJNHZDX Portfolio Turnover 74.60% |
from AlgorithmImports import *
from datetime import datetime
class Magnificent7IntradayStrategy(QCAlgorithm):
def initialize(self):
self.set_start_date(2024, 1, 1)
self.set_end_date(2024, 4, 1)
self.set_cash(1000000)
# Define the MEGA-CAP stocks
self.symbols = [self.add_equity(ticker, Resolution.MINUTE).symbol for ticker in ["TSLA", "AMZN", "NFLX", "AAPL", "NVDA", "GOOGL", "MSFT", "META"]]
# Define indicators
self.rsi_indicators = {symbol: self.rsi(symbol, 14, MovingAverageType.WILDERS, Resolution.MINUTE) for symbol in self.symbols}
self.sma_indicators = {symbol: self.sma(symbol, 200, Resolution.MINUTE) for symbol in self.symbols}
# Schedule trading hours
self.schedule.on(self.date_rules.every_day(), self.time_rules.every(timedelta(minutes=15)), self.trade)
# Risk management
self.set_risk_management(MaximumDrawdownPercentPerSecurity(0.015))
# Track last trade date for each symbol
self.last_trade_date = {symbol: None for symbol in self.symbols}
self.settings.minimum_order_margin_portfolio_percentage = 0
def trade(self):
current_time = self.Time
if current_time.weekday() >= 5 or not (current_time.hour == 9 and current_time.minute >= 45):
return
for symbol in self.symbols:
if not self.rsi_indicators[symbol].is_ready or not self.sma_indicators[symbol].is_ready:
continue
price = self.securities[symbol].price
rsi_value = self.rsi_indicators[symbol].current.value
sma_value = self.sma_indicators[symbol].current.value
# Check if a trade has already been made today
if self.last_trade_date[symbol] == current_time.date():
continue
# Entry condition
if rsi_value < 30 and price < sma_value:
self.set_holdings(symbol, 1)
self.last_trade_date[symbol] = current_time.date()
# Exit conditions
if self.portfolio[symbol].invested:
previous_day_high = self.history(symbol, 1, Resolution.Daily)["high"].iloc[0]
if price >= previous_day_high or rsi_value > 80:
self.liquidate(symbol)
self.last_trade_date[symbol] = current_time.date() # Update trade date on exit